Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

FedDyna: Privacy-Preserving Federated Learning with Dynamic Noise Adaptation and Structural Bias Alignment for Non-IID Environments

Guijuan Wang Zhiyu Zuo An-Ming Dong Jiguo Yu Yan-Qi Zhao
· 0 citations · 30 references

TL;DR

FedDyna is proposed, a novel framework that uses an adaptive noise injection mechanism to enhance privacy in non-IID data environments and effectively demonstrates the significant divergence between dynamic and static noise under non-IID settings.

Semantic Scholar
View source

Similar papers

#federated learning Conference Aug 2026

Federated learning-based privacy-preserving multisource data fusion for smart grids

This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix...

Jia-Ying Li, Can Pei · 0 citations
Open access 2025

Federated Continual Learning for Privacy-Preserving Predictive Intelligence

The Federated Continual Learning framework for Privacy-Preserving Predictive Intelligence (FCL3Pi), which integrates federated optimization with continual learning to enable adaptive, decentralized, and privacy-preserving predictive intelligence, is proposed.

Lothar Collatz · 0 citations
Book Open access Aug 2026

Learning in the Right Subspace: Personalized Differential Private Federated Learning with Noise Filtering

FedSPA, a Subspace Projection Aggregation personalized differential private Federated learning framework, is proposed, which not only effectively guides the aggregation of client personalized differential privacy but also reduces communication overhead.

Tianchi Liao, Xiaojun Deng, Le-Le Fu et al. · 0 citations
Conference Aug 2026

URP-FL: Robust and Personalized Federated Learning under Heterogeneous and Adversarial Conditions

Federated learning is appealing for privacy-sensitive network systems, yet its practical deployment remains hindered by the following three recurring challenges: (1) client drift under non-IID data, (2) vulnerability to corrupted updates, and (3) the communication cost of repeated model exchange. Most existing approach...

Hua Kun, Wei Wang · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.